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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code>.cross_validate</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-model-selection-cross-validate">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_validate</span></code></a></li>
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  <div class="section" id="sklearn-model-selection-cross-validate">
<h1><a class="reference internal" href="../classes.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a>.cross_validate<a class="headerlink" href="#sklearn-model-selection-cross-validate" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.model_selection.cross_validate">
<code class="sig-prename descclassname">sklearn.model_selection.</code><code class="sig-name descname">cross_validate</code><span class="sig-paren">(</span><em class="sig-param">estimator</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">groups=None</em>, <em class="sig-param">scoring=None</em>, <em class="sig-param">cv=None</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">verbose=0</em>, <em class="sig-param">fit_params=None</em>, <em class="sig-param">pre_dispatch='2*n_jobs'</em>, <em class="sig-param">return_train_score=False</em>, <em class="sig-param">return_estimator=False</em>, <em class="sig-param">error_score=nan</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/model_selection/_validation.py#L39"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.model_selection.cross_validate" title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluate metric(s) by cross-validation and also record fit/score times.</p>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#multimetric-cross-validation"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>estimator</strong><span class="classifier">estimator object implementing ‘fit’</span></dt><dd><p>The object to use to fit the data.</p>
</dd>
<dt><strong>X</strong><span class="classifier">array-like</span></dt><dd><p>The data to fit. Can be for example a list, or an array.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, optional, default: None</span></dt><dd><p>The target variable to try to predict in the case of
supervised learning.</p>
</dd>
<dt><strong>groups</strong><span class="classifier">array-like, with shape (n_samples,), optional</span></dt><dd><p>Group labels for the samples used while splitting the dataset into
train/test set. Only used in conjunction with a “Group” <a class="reference internal" href="../../glossary.html#term-cv"><span class="xref std std-term">cv</span></a>
instance (e.g., <a class="reference internal" href="sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>).</p>
</dd>
<dt><strong>scoring</strong><span class="classifier">string, callable, list/tuple, dict or None, default: None</span></dt><dd><p>A single string (see <a class="reference internal" href="../model_evaluation.html#scoring-parameter"><span class="std std-ref">The scoring parameter: defining model evaluation rules</span></a>) or a callable
(see <a class="reference internal" href="../model_evaluation.html#scoring"><span class="std std-ref">Defining your scoring strategy from metric functions</span></a>) to evaluate the predictions on the test set.</p>
<p>For evaluating multiple metrics, either give a list of (unique) strings
or a dict with names as keys and callables as values.</p>
<p>NOTE that when using custom scorers, each scorer should return a single
value. Metric functions returning a list/array of values can be wrapped
into multiple scorers that return one value each.</p>
<p>See <a class="reference internal" href="../grid_search.html#multimetric-grid-search"><span class="std std-ref">Specifying multiple metrics for evaluation</span></a> for an example.</p>
<p>If None, the estimator’s score method is used.</p>
</dd>
<dt><strong>cv</strong><span class="classifier">int, cross-validation generator or an iterable, optional</span></dt><dd><p>Determines the cross-validation splitting strategy.
Possible inputs for cv are:</p>
<ul class="simple">
<li><p>None, to use the default 5-fold cross validation,</p></li>
<li><p>integer, to specify the number of folds in a <code class="docutils literal notranslate"><span class="pre">(Stratified)KFold</span></code>,</p></li>
<li><p><a class="reference internal" href="../../glossary.html#term-cv-splitter"><span class="xref std std-term">CV splitter</span></a>,</p></li>
<li><p>An iterable yielding (train, test) splits as arrays of indices.</p></li>
</ul>
<p>For integer/None inputs, if the estimator is a classifier and <code class="docutils literal notranslate"><span class="pre">y</span></code> is
either binary or multiclass, <a class="reference internal" href="sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedKFold</span></code></a> is used. In all
other cases, <a class="reference internal" href="sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a> is used.</p>
<p>Refer <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for the various
cross-validation strategies that can be used here.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.22: </span><code class="docutils literal notranslate"><span class="pre">cv</span></code> default value if None changed from 3-fold to 5-fold.</p>
</div>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of CPUs to use to do the computation.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">integer, optional</span></dt><dd><p>The verbosity level.</p>
</dd>
<dt><strong>fit_params</strong><span class="classifier">dict, optional</span></dt><dd><p>Parameters to pass to the fit method of the estimator.</p>
</dd>
<dt><strong>pre_dispatch</strong><span class="classifier">int, or string, optional</span></dt><dd><p>Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:</p>
<blockquote>
<div><ul class="simple">
<li><p>None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs</p></li>
<li><p>An int, giving the exact number of total jobs that are
spawned</p></li>
<li><p>A string, giving an expression as a function of n_jobs,
as in ‘2*n_jobs’</p></li>
</ul>
</div></blockquote>
</dd>
<dt><strong>return_train_score</strong><span class="classifier">boolean, default=False</span></dt><dd><p>Whether to include train scores.
Computing training scores is used to get insights on how different
parameter settings impact the overfitting/underfitting trade-off.
However computing the scores on the training set can be computationally
expensive and is not strictly required to select the parameters that
yield the best generalization performance.</p>
</dd>
<dt><strong>return_estimator</strong><span class="classifier">boolean, default False</span></dt><dd><p>Whether to return the estimators fitted on each split.</p>
</dd>
<dt><strong>error_score</strong><span class="classifier">‘raise’ or numeric</span></dt><dd><p>Value to assign to the score if an error occurs in estimator fitting.
If set to ‘raise’, the error is raised.
If a numeric value is given, FitFailedWarning is raised. This parameter
does not affect the refit step, which will always raise the error.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl>
<dt><strong>scores</strong><span class="classifier">dict of float arrays of shape (n_splits,)</span></dt><dd><p>Array of scores of the estimator for each run of the cross validation.</p>
<p>A dict of arrays containing the score/time arrays for each scorer is
returned. The possible keys for this <code class="docutils literal notranslate"><span class="pre">dict</span></code> are:</p>
<blockquote>
<div><dl class="simple">
<dt><code class="docutils literal notranslate"><span class="pre">test_score</span></code></dt><dd><p>The score array for test scores on each cv split.
Suffix <code class="docutils literal notranslate"><span class="pre">_score</span></code> in <code class="docutils literal notranslate"><span class="pre">test_score</span></code> changes to a specific
metric like <code class="docutils literal notranslate"><span class="pre">test_r2</span></code> or <code class="docutils literal notranslate"><span class="pre">test_auc</span></code> if there are
multiple scoring metrics in the scoring parameter.</p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">train_score</span></code></dt><dd><p>The score array for train scores on each cv split.
Suffix <code class="docutils literal notranslate"><span class="pre">_score</span></code> in <code class="docutils literal notranslate"><span class="pre">train_score</span></code> changes to a specific
metric like <code class="docutils literal notranslate"><span class="pre">train_r2</span></code> or <code class="docutils literal notranslate"><span class="pre">train_auc</span></code> if there are
multiple scoring metrics in the scoring parameter.
This is available only if <code class="docutils literal notranslate"><span class="pre">return_train_score</span></code> parameter
is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">fit_time</span></code></dt><dd><p>The time for fitting the estimator on the train
set for each cv split.</p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">score_time</span></code></dt><dd><p>The time for scoring the estimator on the test set for each
cv split. (Note time for scoring on the train set is not
included even if <code class="docutils literal notranslate"><span class="pre">return_train_score</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code></p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">estimator</span></code></dt><dd><p>The estimator objects for each cv split.
This is available only if <code class="docutils literal notranslate"><span class="pre">return_estimator</span></code> parameter
is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
</dd>
</dl>
</div></blockquote>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_val_score</span></code></a></dt><dd><p>Run cross-validation for single metric evaluation.</p>
</dd>
<dt><a class="reference internal" href="sklearn.model_selection.cross_val_predict.html#sklearn.model_selection.cross_val_predict" title="sklearn.model_selection.cross_val_predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_val_predict</span></code></a></dt><dd><p>Get predictions from each split of cross-validation for diagnostic purposes.</p>
</dd>
<dt><a class="reference internal" href="sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.make_scorer</span></code></a></dt><dd><p>Make a scorer from a performance metric or loss function.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">linear_model</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_validate</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">diabetes</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_diabetes</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">diabetes</span><span class="o">.</span><span class="n">data</span><span class="p">[:</span><span class="mi">150</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">diabetes</span><span class="o">.</span><span class="n">target</span><span class="p">[:</span><span class="mi">150</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">lasso</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Lasso</span><span class="p">()</span>
</pre></div>
</div>
<p>Single metric evaluation using <code class="docutils literal notranslate"><span class="pre">cross_validate</span></code></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span><span class="n">lasso</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">cv_results</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="go">[&#39;fit_time&#39;, &#39;score_time&#39;, &#39;test_score&#39;]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cv_results</span><span class="p">[</span><span class="s1">&#39;test_score&#39;</span><span class="p">]</span>
<span class="go">array([0.33150734, 0.08022311, 0.03531764])</span>
</pre></div>
</div>
<p>Multiple metric evaluation using <code class="docutils literal notranslate"><span class="pre">cross_validate</span></code>
(please refer the <code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter doc for more information)</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span><span class="n">lasso</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="gp">... </span>                        <span class="n">scoring</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;r2&#39;</span><span class="p">,</span> <span class="s1">&#39;neg_mean_squared_error&#39;</span><span class="p">),</span>
<span class="gp">... </span>                        <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">[</span><span class="s1">&#39;test_neg_mean_squared_error&#39;</span><span class="p">])</span>
<span class="go">[-3635.5... -3573.3... -6114.7...]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">[</span><span class="s1">&#39;train_r2&#39;</span><span class="p">])</span>
<span class="go">[0.28010158 0.39088426 0.22784852]</span>
</pre></div>
</div>
</dd></dl>

<div class="section" id="examples-using-sklearn-model-selection-cross-validate">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_validate</span></code><a class="headerlink" href="#examples-using-sklearn-model-selection-cross-validate" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individu..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_stack_predictors_thumb.png" src="../../_images/sphx_glr_plot_stack_predictors_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
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